@inproceedings{mondal-etal-2025-adaptive,
title = "{ADAPTIVE} {IE}: Investigating the Complementarity of Human-{AI} Collaboration to Adaptively Extract Information on-the-fly",
author = "Mondal, Ishani and
Yuan, Michelle and
N, Anandhavelu and
Garimella, Aparna and
Ferraro, Francis and
Blair-Stanek, Andrew and
Van Durme, Benjamin and
Boyd-Graber, Jordan",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.392/",
pages = "5870--5889",
abstract = "Information extraction (IE) needs vary over time, where a flexible information extraction (IE) system can be useful. Despite this, existing IE systems are either fully supervised, requiring expensive human annotations, or fully unsupervised, extracting information that often do not cater to user`s needs. To address these issues, we formally introduce the task of {\textquotedblleft}IE on-the-fly{\textquotedblright}, and address the problem using our proposed Adaptive IE framework that uses human-in-the-loop refinement to adapt to changing user questions. Through human experiments on three diverse datasets, we demonstrate that Adaptive IE is a domain-agnostic, responsive, efficient framework for helping users access useful information while quickly reorganizing information in response to evolving information needs."
}
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<abstract>Information extraction (IE) needs vary over time, where a flexible information extraction (IE) system can be useful. Despite this, existing IE systems are either fully supervised, requiring expensive human annotations, or fully unsupervised, extracting information that often do not cater to user‘s needs. To address these issues, we formally introduce the task of “IE on-the-fly”, and address the problem using our proposed Adaptive IE framework that uses human-in-the-loop refinement to adapt to changing user questions. Through human experiments on three diverse datasets, we demonstrate that Adaptive IE is a domain-agnostic, responsive, efficient framework for helping users access useful information while quickly reorganizing information in response to evolving information needs.</abstract>
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%0 Conference Proceedings
%T ADAPTIVE IE: Investigating the Complementarity of Human-AI Collaboration to Adaptively Extract Information on-the-fly
%A Mondal, Ishani
%A Yuan, Michelle
%A N, Anandhavelu
%A Garimella, Aparna
%A Ferraro, Francis
%A Blair-Stanek, Andrew
%A Van Durme, Benjamin
%A Boyd-Graber, Jordan
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F mondal-etal-2025-adaptive
%X Information extraction (IE) needs vary over time, where a flexible information extraction (IE) system can be useful. Despite this, existing IE systems are either fully supervised, requiring expensive human annotations, or fully unsupervised, extracting information that often do not cater to user‘s needs. To address these issues, we formally introduce the task of “IE on-the-fly”, and address the problem using our proposed Adaptive IE framework that uses human-in-the-loop refinement to adapt to changing user questions. Through human experiments on three diverse datasets, we demonstrate that Adaptive IE is a domain-agnostic, responsive, efficient framework for helping users access useful information while quickly reorganizing information in response to evolving information needs.
%U https://aclanthology.org/2025.coling-main.392/
%P 5870-5889
Markdown (Informal)
[ADAPTIVE IE: Investigating the Complementarity of Human-AI Collaboration to Adaptively Extract Information on-the-fly](https://aclanthology.org/2025.coling-main.392/) (Mondal et al., COLING 2025)
ACL
- Ishani Mondal, Michelle Yuan, Anandhavelu N, Aparna Garimella, Francis Ferraro, Andrew Blair-Stanek, Benjamin Van Durme, and Jordan Boyd-Graber. 2025. ADAPTIVE IE: Investigating the Complementarity of Human-AI Collaboration to Adaptively Extract Information on-the-fly. In Proceedings of the 31st International Conference on Computational Linguistics, pages 5870–5889, Abu Dhabi, UAE. Association for Computational Linguistics.